Punctuated Equilibria in Artificial Intelligence: The Institutional Scaling Law and the Speciation of Sovereign AI

Researchers challenge the assumption of continuous AI progress, proposing that AI development follows punctuated equilibrium patterns with rapid phase transitions. They introduce the Institutional Scaling Law, proving that larger AI models don't always perform better in institutional environments due to trust, cost, and compliance factors.

• →AI development proceeds through periods of stasis interrupted by rapid phase transitions rather than continuous progress.
• →The Institutional Scaling Law proves that institutional fitness is non-monotonic in model scale, contradicting classical scaling laws.
• →Smaller, domain-adapted AI systems can mathematically outperform large frontier models in most institutional deployment environments.
• →AI systems are evaluated along four dimensions: capability, institutional trust, affordability, and sovereign compliance.
• →The rise of sovereign AI represents a new geopolitical selection pressure influencing AI development.